similar to: Tail area of sum of Chi-square variables

Dear R-Users, I need to find a smooth function f() and coefficients a_i that give the best fit to y ~ a_0 + a_1*f(x_1) + a_2*f(x_2) Note that it is the same non-linear transformation f() that is applied to both x_1 and x_2. So my first question is how can I do it in R? A more general question is this: suppose I have a utility function U(a_i, f()), where f() is say a spline. Is there a general

Dear All, I've been searching relevant topics about replacing values, none seemed to be applicable to me... I have a file with many many varieties, and want to replace some of them into different names. I tried various of ways, still don't know how to do that most efficiently.. Here is part of the example data: Gen Rep A_1 1 A_1 2 A_2 1 A_2 2 B_1 1 B_1

Hi all, I have n independent variables A_1, A_2, A_3,......,A_n, and each with known variances var(A_1), var(A_2),..., but unknown mean. How can I get the approximation of the variance of the product of the variables using numerical computation, i.e. var(A_1*A_2*A_3*.....*A_n)? Thanks. Sincerely, Yanwei Zhang Department of Actuarial Research and Modeling Munich Re America Tel: 609-275-2176

Hi, could anyone tell me how predict() predicts the new value(s), of a MA(1) arima-modell. its really easy to make it with an AR(1), knowing the last term, but how can i or R know the last error? It would also help if somebody could tell me how to find the "open" source of the function predict(). Thanks and sorry for my poor english. -- View this message in context:

Hello, I'm very new to using R, but I was told it could do what I want. I'm not sure how best to enter the information but here goes... I'm trying to transfer the following integral into R to solve for ln(gamma_1), on the left, for multiple instances of gamma_i and variable N_i. gamma_i is, for example, (0, 0.03012048, 0.05000000, 0.19200000, 0.44000000, 0.62566845) N_i (N_1 or

Dear All, I have six different campaign and 5 different agent have login on that campaign.*Same thing i have done using agi and database,i never use queue management on this scenario. Agent** can also shuffling one campaign to anther campaign. * Now i want to do some work with queue.I want to use single queue to managing this. Eg: campaign Agent Login A a_1,a_3

Hello, I am looking into description of surf.ls(spatial) and see under value $beta - the coefficients. When I use polynomial of degree 2 to fit surface I expect to get 4 coefficients: z = a_1 x^2 + a_2 xy + a_3 y^2 + a_4 What do beta really stand for and why do I get $beta vector of length 6? Thakns, Mark

Hi I have two arbitrarily dimensioned arrays, "a" and "b", with length(dim(a))==length(dim(b)). I want to form a sort of "corner-to-corner" version of abind(), or a multidimensional version of blockdiag(). In the case of matrices, the function is easy to write and if a=matrix(1,3,4) and b=matrix(2,2,2), then adiag(a,b) would return: [,1] [,2] [,3] [,4] [,5]

Hi! I want to write portions of my data (3573 columns at a time) to twenty folders I have available titled "A_1" to "A_20" such that the first 3573 columns will go to folder A_1, next 3573 to folder A_2 and so on. This code below ensures that the data is written into all 20 folders, but only the last iteration of the loop (last 3573 columns) is being written into ALL of the

Hello to all, I am having trouble with intregrating a complicated uni-dimensional function of the following form Phi(x-a_1)*Phi(x-a_2)*...*Phi(x-a_{n-1})*phi(x-a_n). Here n is about 5000, Phi is the cumulative distribution function of standard normal, phi is the density function of standard normal, and x ranges over (-infty,infty). My idea is to to use quadrature to handle this integral. But

Hallo everybody, I'm new in r and I"ll appreciate some help! I have a matrix of nrow=30 and ncoll=54,and I would like to generate 50 simulations with tha same size of the matrix!!!That is to say that I want to generate 50 matrices -for my 50 simulations - with the same dimensions! I took my 1st matrix according to the formula that I want to implement: D<-mean_m + U_i*mat_DELTA

Dear All, I am wondering on how to use the abline.xyplot with xyplot such that I will have different vertical lines for each panel. More sepcifically, suppose that the xyplot generates 4 panels defined by the combination of two binary variables: X_1 and X_2. i.e. xyplot(Y ~ Z | X_1*X_2, data = df) I want something like: abline(v = 5) if X_1=0 and X_2 = 0 abline(v =

Hi All Apologies if this is not the correct list for this question. The Rglpk package offers the following example in its documentation library(Rglpk) ## Simple mixed integer linear program. ## maximize: 3 x_1 + 1 x_2 + 3 x_3 ## subject to: -1 x_1 + 2 x_2 + x_3 <= 4 ## 4 x_2 - 3 x_3 <= 2 ## x_1 - 3 x_2 + 2 x_3 <= 3 ## x_1, x_3 are non-negative integers ## x_2 is a non-negative real

Hi all, I'd like to fit a multivariate regression with the variance of the error term porportional to the predictors, like the WLS in the univariate case. y_1~x_1+x_2 y_2~x_1+x_2 var(y_1)=x_1*sigma_1^2 var(y_2)=x_2*sigma_2^2 cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 How can I specify this in R? Is there a corresponding function to the univariate specification lm(y~x,weights=x)??

Dear R-users, i try to solve to following linear programm in R 0 * x_1 + 2/3 * x_2 + 1/3 * x_3 + 1/3 * x_4 = 0.3 x_1 + x_2 + x_3 + x_4 = 1 x_1, x_2, x_3, x_4 > 0, x_1, x_2, x_3, x_4 < 1 as you can see i have no objective function here besides that i use the following code. library(lpSolve) f.obj<-c(1,1,1,1) f.con<-matrix(c(0,2/3,1/3,1/3, 1,1,1,1,

Dear List: I cannot seem to find a way to plot my data correctly. I have a small data frame with 6 total variables (x_1 ... x_6). I am trying to plot x_1 against x_2 and x_3. I have tried plot(x_2, x_1) #obviously works fine plot(x_3, x_1, add=TRUE) # Does not work. I keep getting error messages. I would also like to add ablines to this plot. I have experimented with a number of other

Dear All, Here is what I am trying to achieve: I would like to plot some data in 3D. Usually, one has a matrix of the kind y_1(x_1) , y_1(x_2).....y_1(x_i) y_2(x_1) , y_2(x_2).....y_2(x_i) ........................................... y_n(x_1) , y_n(x_2)......y_n(x_i) where e.g. y_2(x_1) is the value of y at time 2 at point x_1 (see that the grid in x is the same for the y values at all times).

> I asked myself the same question. Without mod, how do you ensure that for instance the expression 2*i+255 was not actually 2*i-1 ? I think it is not possible in general, but I believe it is possible in case of affine expressions used as GEP indices. I assume, GEP indices (except indexing into struct) are interpreted as signed integers. It isn't explicitly stated in the LangRef, but

Hi there! Today I tried to estimate models using both plm and pgmm functions, with an interaction between X1 and lag(X2, 1). And I notice two issues. Let "Y=b_1 * X_1 + b_2 * X_2 + b_3 * X_1 * x_2 + e" be our model. 1) When using plm, I got different results when I coded the interaction term with I(X1 * lag(X2, 1)) and when I just saved this multiplication X1 * lag(X2, 1) in a

Hi,   I have a system in which I analyze 2 subjects and 1 variable, so I have 2 models as follow:   y ~ x_1[, 1] + x_2[, 1] + x_1[, 2] + x_2[, 2]   Where   x_1[, i] = cos(2 * pi * t / T_i) x_2[, i] = sin(2 * pi * t / T_i)   i = 1, 2   Data have two columns: t and y.   As you can see, I have a multiple components model, with rithm and without trends, and I have a fundamental